Type-Agnostic RF Signal Representations

ABSTRACT

This document describes techniques and devices for type-agnostic radio frequency (RF) signal representations. These techniques and devices enable use of multiple different types of radar systems and fields through type-agnostic RF signal representations. By so doing, recognition and application-layer analysis can be independent of various radar parameters that differ between different radar systems and fields.

RELATED APPLICATIONS

This application is a continuation application claiming priority under35 U.S.C. § 120 to U.S. patent application Ser. No. 15/142,829 file Apr.29, 2016, which claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application Ser. No. 62/155,357 filed Apr. 30, 2015,and U.S. Provisional Patent Application Ser. No. 62/237,750 filed Oct.6, 2015, the disclosures of which are incorporated by reference hereinin their entirety.

BACKGROUND

Small-screen computing devices continue to proliferate, such assmartphones and computing bracelets, rings, and watches. Like manycomputing devices, these small-screen devices often use virtualkeyboards to interact with users. On these small screens, however, manypeople find interacting through virtual keyboards to be difficult, asthey often result in slow and inaccurate inputs. This frustrates usersand limits the applicability of small-screen computing devices. Thisproblem has been addressed in part through screen-based gesturerecognition techniques. These screen-based gestures, however, stillstruggle from substantial usability issues due to the size of thesescreens.

To address this problem, optical finger- and hand-tracking techniqueshave been developed, which enable gesture tracking not made on thescreen. These optical techniques, however, have been large, costly, orinaccurate thereby limiting their usefulness in addressing usabilityissues with small-screen computing devices.

Furthermore, control through gestures continues to proliferate for otherdevices and uses, such as from mid to great distances. People not onlywish to control devices near to them, but also those from medium tolarge distances, such as to control a stereo across a room, a thermostatin a different room, or a television that is a few meters away.

SUMMARY

This document describes techniques and devices for type-agnostic radiofrequency (RF) signal representations. These techniques and devicesenable use of multiple different types of radar systems and fieldsthrough a standard set of type-agnostic RF signal representations. By sodoing, recognition and application-layer analysis can be independent ofvarious radar parameters that differ between different radar systems andfields.

Through use of these techniques and devices, a large range of gestures,both in size of the gestures and distance from radar sensors, can beused. Even a single device having different radar systems, for example,can recognize these gestures with gesture analysis independent of thedifferent radar systems. Gestures of a person sitting on a couch tocontrol a television, standing in a kitchen to control an oven orrefrigerator, centimeters from a computing watch's small-screen displayto control an application, or even an action of a person walking out ofa room causing the lights to turn off—all can be recognized without aneed to build type-specific recognition and application-layer analysis.

This summary is provided to introduce simplified concepts concerningtype-agnostic RF signal representations, which is further describedbelow in the Detailed Description. This summary is not intended toidentify essential features of the claimed subject matter, nor is itintended for use in determining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of techniques and devices for type-agnostic RF signalrepresentations are described with reference to the following drawings.The same numbers are used throughout the drawings to reference likefeatures and components:

FIG. 1 illustrates an example environment in which techniques enablingtype-agnostic RF signal representations may be embodied. The environmentillustrates 1 to N different type-specific radar systems, an abstractionmodule, and a gesture module.

FIG. 2 illustrates an example of the abstraction module of FIG. 1 indetail.

FIG. 3 illustrates an example of the gesture module of FIG. 1 in detail.

FIG. 4 illustrates a computing device through which determination oftype-agnostic RF signal representations can be enabled.

FIG. 5 illustrates an example method enabling gesture recognitionthrough determination of type-agnostic RF signal representations.

FIG. 6 illustrates example different radar fields of FIG. 1.

FIG. 7 illustrates an example computing system embodying, or in whichtechniques may be implemented that enable use of, type-agnostic RFsignal representations.

DETAILED DESCRIPTION Overview

This document describes techniques and devices enabling type-agnostic RFsignal representations. These techniques and devices enable a greatbreadth of actions and gestures sensed through different radar types orfields, such as gestures to use, control, and interact with variousdevices, from smartphones to refrigerators. The techniques and devicesare capable of doing so without needing to build type-specificrecognition and application-layer analysis.

Consider FIG. 1, which illustrates an example environment 100 in whichtechniques enabling type-agnostic RF signal representations may beembodied. Environment 100 includes different type-specific radar systems102, shown with some number from 1 to N systems, labeled type-specificradar systems 102-1, 102-2, and 102-N. These type-specific radar systems102 may include various types of radar systems that can provide a widevariety of radar fields, such as single tone, stepped frequencymodulated, linear frequency modulated, impulse, or chirped.

Each of these type-specific radar systems 102 provide different radarfields through differently structured or differently operatedradar-emitting elements 104, shown with 104-1, 104-2, and 104-N. Theseradar fields may differ as noted herein, and may have differentmodulation, frequency, amplitude, or phase. Each of these type-specificradar systems 102 also includes an antenna element 106, and in somecases a pre-processor 108, labeled antenna elements 106-1, 106-2, and106-N, and pre-preprocessor 108-1, 108-2, and 108-N, both respectively.

Each of these type-specific radar systems 102 emit radar to provide aradar field 110, and then receive reflection signals 112 from an objectmoving in the radar field 110. Here three human hands are shown, eachperforming a different gesture, a hand wave gesture 114, a fist shakegesture 116 (an American Sign Language (ASL) gesture for “Yes”), and apinch finger gesture 118, though the techniques are not limited to humanhands or gestures.

As shown, each of the type-specific radar systems 102 providestype-specific raw data 120 responsive to receiving the reflection signal112 (only one system shown receiving the reflection signal 112 forvisual brevity). Each of the type-specific radar systems 102 providetype-specific raw data 120, shown as raw data 120-1, 120-2, and 120-N,respectively for each system. Each of these raw data 120 can, but do nothave to be, a raw digital sample on which pre-processing by thepre-processor 108 of the type-specific radar system 102 has beenperformed.

These type-specific raw data 120 are received by an abstraction module122. Generally, the abstraction module 122 transforms each of thedifferent types of type-specific raw data 120 into a type-agnosticsignal representation 124, shown as type-agnostic signal representation124-1, 124-2, and 124-N, respectively for each of the type-specific rawdata 120-1, 120-2, and 120-N. These type-agnostic signal representations124 are then received by recognition module 126. Generally, therecognition module 126 determines, for each of the type-agnostic signalrepresentations 124, a gesture 128 or action of the object within therespective two or more different radar fields. Each of these gestures128 is shown as gesture 128-1, 128-2, and 128-N, respectively, for eachof the type-agnostic signal representations 124-1, 124-2, and 124-N.With the gesture 128 or action determined, the recognition module 126passes each gesture 128 or action to another entity, such as anapplication executing on a device to control the application. Note thatin some cases a single gesture or action is determined for multipledifferent raw data 120, and thus multiple different type-agnostic signalrepresentations 124, such as in a case where two radar systems or fieldsare simultaneously used to sense a movement of a person in differentradar fields. Functions and capabilities of the abstraction module 122are described in greater detail as part of FIG. 2 and of the recognitionmodule 126 as part of FIG. 3.

Example Abstraction Module

FIG. 2 illustrates an example of the abstraction module 122 of FIG. 1.The abstraction module 122 receives one or more of the type-specific rawdata 120 and outputs, for each of the raw data 120-1, 120-2, through120-N, a type-agnostic signal representation 124-1, 124-2, through124-N, respectively. In some cases, the raw data 120 is first processedby raw signal processor 202, which is configured to provide a complexsignal based on the type-specific raw data 120 where the complex signalcontains amplitude and phase information from which a phase of thetype-specific raw data 120 can be extracted and unwrapped. Examplestypes of processing include, for impulse radar (a type of low-powerultra-wideband radar), a smoothing bandpass filter and a Hilberttransform. Processing for frequency-modulated continuous-wave (FM-CW)radar includes windowing filtering and range fast-Fourier transforming(FFT). Further still, processing by the raw signal processor 202 can beconfigured to pulse shape filter and pulse compress binary phase-shiftkeying (BPSK) radar.

Whether processed by the raw signal processor 202 or received as thetype-specific raw data 120, a signal transformer 204 acts to transformraw data (processed or otherwise) into the type-agnostic signalrepresentation 124. Generally, the signal transformer is configured tomodel the object captured by the raw data as a set of scattering centerswhere each of the set of scattering centers having a reflectivity thatis dependent on a shape, size, aspect, or material of the object thatmakes a movement to perform a gesture or action. To do so, the signaltransformer 204 may extract object properties and dynamics from thetype-specific raw data 120 as a function of fast time (e.g., with eachacquisition) and slow time (e.g., across multiple acquisitions) or atransient or late-time electromagnetic (EM) response of the set ofscattering centers.

This is illustrated with four example transforms, which may be usedalone or in conjunction. These include transforming the data into arange-Doppler-time profile 206, a range-time profile 208, amicro-Doppler profile 210, and a fast-time spectrogram 212. Therange-Doppler-time profile 206 resolves scattering centers in range andvelocity dimensions. The range-time profile 208 is a time history ofrange profiles. The micro-Doppler profile 210 is time history of Dopplerprofiles. The fast-time spectrogram 212 identifiesfrequency/target-dependent signal fading and resonances. Each of thesetransforms are type-agnostic signal representations, though thetype-agnostic signal representation 124 may include one or more of each.

Example Gesture Module

As noted above, functions and capabilities of the recognition module 126are described in more detail as part of FIG. 3. As shown, FIG. 3illustrates an example of the recognition module 126 of FIG. 1, whichincludes a feature extractor 302 and a gesture recognizer 304.Generally, the recognition module 126 receives the type-agnostic signalrepresentation 124 (shown with 1 to N signal representations, though asfew as one can be received and recognized) and determines, based on thetype-agnostic signal representation 124, a gesture or action of theobject within the respective different type of type-specific radar fieldfrom which the type-agnostic signal representation 124 was determined.In more detail, the feature extractor 302 is configured to extracttype-agnostic features, such as signal transformations, engineeredfeatures, computer-vision features, machine-learned features, orinferred target features.

In more detail, the gesture recognizer 304 is configured to determineactions or gestures performed by the object, such as walking out of aroom, sitting, or gesturing to change a channel, turn down a mediaplayer, or turn off an oven, for example. To do so, the gesturerecognizer 304 can determine a gesture classification, motion parametertracking, regression estimate, or gesture probability based on thetype-agnostic signal representation 124 or the post-extracted featuresfrom the feature extractor 302. The gesture recognizer 304 may also mapthe gesture 128 to a pre-configured control gesture associated with acontrol input for the application and/or device 306. The recognitionmodule 126 then passes each determined gesture 128 (shown with 1 to Ngestures, though as few as one can be determined) effective to controlan application and/or device 306, such as to control or alter a userinterface on a display, a function, or a capability of a device. Asshown in FIG. 1, these gestures may include gestures of a human hand,such as the hand wave gesture 114, the fist shake gesture 116, and thepinch finger gesture 118 to name but a few.

As noted above, the techniques for determining type-agnostic RF signalrepresentations permit recognition and application-layer analysis to beindependent of various radar parameters that differ between differentradar systems and fields. This enables few or none of the elements ofFIG. 3 to be specific to a particular radar system. Thus, therecognition module 126 need not be specific to the type of radar field,or built to accommodate one or even any types of radar fields. Further,the application and/or device 306 need not require application-layeranalysis. The recognition module 126 and the application and/or device306 may therefore by universal to many different types of radar systemsand fields.

This document now turns to an example computing device in whichtype-agnostic RF signal representations can be used, and then followswith an example method and example radar fields, and ends with anexample computing system.

Example Computing Device

FIG. 4 illustrates a computing device through which type-agnostic RFsignal representations can be enabled. Computing device 402 isillustrated with various non-limiting example devices, desktop computer402-1, computing watch 402-2, smartphone 402-3, tablet 402-4, computingring 402-5, computing spectacles 402-6, and microwave 402-7, thoughother devices may also be used, such as home automation and controlsystems, entertainment systems, audio systems, other home appliances,security systems, netbooks, automobiles, and e-readers. Note that thecomputing device 402 can be wearable, non-wearable but mobile, orrelatively immobile (e.g., desktops and appliances).

The computing device 402 includes one or more computer processors 404and computer-readable media 406, which includes memory media and storagemedia. Applications and/or an operating system (not shown) embodied ascomputer-readable instructions on computer-readable media 406 can beexecuted by processors 404 to provide some of the functionalitiesdescribed herein. Computer-readable media 406 also includes theabstraction module 122 and the recognition module 126, and may alsoinclude each of their optional components, the raw signal processor 202,the signal transformer 204, the feature extractor 302, and the gesturerecognizer 304 (described above).

The computing device 402 may also include one or more network interfaces408 for communicating data over wired, wireless, or optical networks anda display 410. By way of example and not limitation, the networkinterface 408 may communicate data over a local-area-network (LAN), awireless local-area-network (WLAN), a personal-area-network (PAN), awide-area-network (WAN), an intranet, the Internet, a peer-to-peernetwork, point-to-point network, a mesh network, and the like. Thedisplay 410 can be integral with the computing device 402 or associatedwith it, such as with the desktop computer 402-1.

The computing device 402 is also shown including one or moretype-specific radar systems 102 from FIG. 1. As noted, thesetype-specific radar systems 102 each provide different types of theradar fields 110, whether by different types of radar-emitting elements104 or different ways of using as little as one type of radar-emittingelement 104, and thus provide different types of raw data 120.

In more detail, the different types of the radar fields 110 may includecontinuous wave and pulsed radar systems, and fields for close or farrecognition, or for line-of-sight or obstructed use. Pulsed radarsystems are often of shorter transmit time and higher peak power, andinclude both impulse and chirped radar systems. Pulsed radar systemshave a range based on time of flight and a velocity based on frequencyshift. Chirped radar systems have a range based on time of flight (pulsecompressed) and a velocity based on frequency shift. Continuous waveradar systems are often of relatively longer transmit time and lowerpeak power. These continuous wave radar systems include single tone,linear frequency modulated (FM), and stepped FM types. Single tone radarsystems have a limited range based on the phase and a velocity based onfrequency shift. Linear FM radar systems have a range based on frequencyshift and a velocity also based on frequency shift. Stepped FM radarsystems have a range based on phase or time of flight and a velocitybased on frequency shift. While these five types of radar systems arenoted herein, others may also be used, such as sinusoidal modulationscheme radar systems.

These radar fields 110 can vary from a small size, such as between oneand fifty millimeters, to one half to five meters, to even one to about30 meters. In the larger-size fields, the antenna element 106 can beconfigured to receive and process reflections of the radar field toprovide large-body gestures based on reflections from human tissuecaused by body, arm, or leg movements, though smaller and more-precisegestures can be sensed as well. Example larger-sized radar fieldsinclude those in which a user makes gestures to control a televisionfrom a couch, change a song or volume from a stereo across a room, turnoff an oven or oven timer (a near field would also be useful), turnlights on or off in a room, and so forth.

Note also that the type-specific radar systems 102 can be used with, orembedded within, many different computing devices or peripherals, suchas in walls of a home to control home appliances and systems (e.g.,automation control panel), in automobiles to control internal functions(e.g., volume, cruise control, or even driving of the car), or as anattachment to a laptop computer to control computing applications on thelaptop.

The radar-emitting element 104 can be configured to provide a narrow orwide radar field from little if any distance from a computing device orits display, including radar fields that are a full contiguous field incontrast to beam-scanning radar field. The radar-emitting element 104can be configured to provide the radars of the various types set forthabove. The antenna element 106 is configured to receive reflections of,or sense interactions in, the radar field. In some cases, reflectionsinclude those from human tissue that is within the radar field, such asa hand or arm movement. The antenna element 106 can include one or manyantennas or sensors, such as an array of radiation sensors, the numberin the array based on a desired resolution and whether the field is asurface or volume.

Example Method

FIG. 5 depicts a method 500 that recognizes gestures and actions usingtype-agnostic RF signal representations. The method 500 receivestype-specific raw data from one or more different types of radar fields,and then transforms those type-specific raw data into type-agnosticsignal representations, which are then used to determine gestures oractions within the respective different radar fields. This method isshown as sets of blocks that specify operations performed but are notnecessarily limited to the order or combinations shown for performingthe operations by the respective blocks. In portions of the followingdiscussion reference may be made to environment 100 of FIG. 1 and asdetailed in FIG. 2 or 3, reference to which is made for example only.The techniques are not limited to performance by one entity or multipleentities operating on one device.

In more detail, the method 500, at 502, receives different types oftype-specific raw data representing two or more different reflectionsignals. These two or more different reflection signals, as noted above,are each reflected from an object moving in each of two or moredifferent radar fields. These reflection signals can be received at asame or nearly same time for one movement in two radar fields or twodifferent movements in two different fields at different times. Thesedifferent movements and times can include, for example, a micro-movementof two fingers to control a smart watch and a large gesture to control astereo in another room, with one movement made today and anotheryesterday. While different types of radar systems are illustrated inFIG. 1, the different radar fields can be provided through even a sameradar system that follows two or more modulation schemes.

By way of example, consider six different radar fields 110, shown atradar fields 602, 604, 606, 608, 610, and 612 of FIG. 6. While difficultto show differences at the granular level of modulations schemes and soforth, FIG. 6 illustrates some of the different applications of theseradar fields, from close to far, and from high resolution to low, and soforth. The radar fields 602, 604, and 606 include three similar radarfields for detecting user actions and gestures, such as walking in orout of a room, making a large gesture to operate a game on a televisionor computer, and a smaller gesture for controlling a thermostat or oven.The radar field 608 shows a smaller field for control of a computingwatch by a user's other hand that is not wearing the watch. The radarfield 610 shows a non-volumetric radar field for control by a user'shand that is wearing the computing watch.

The radar field 612 shows an intermediate-sized radar field enablingcontrol of a computer at about ½ to 3 meters.

These radar fields 602 to 612 enable a user to perform complex or simplegestures with his or her arm, body, finger, fingers, hand, or hands (ora device like a stylus) that interrupts the radar field. Examplegestures include the many gestures usable with current touch-sensitivedisplays, such as swipes, two-finger pinch, spread, rotate, tap, and soforth. Other gestures are enabled that are complex, or simple butthree-dimensional, examples include the many sign-language gestures,e.g., those of American Sign Language (ASL) and other sign languagesworldwide. A few examples of these are: an up-and-down fist, which inASL means “Yes”; an open index and middle finger moving to connect to anopen thumb, which means “No”; a flat hand moving up a step, which means“Advance”; a flat and angled hand moving up and down, which means“Afternoon”; clenched fingers and open thumb moving to open fingers andan open thumb, which means “taxicab”; an index finger moving up in aroughly vertical direction, which means “up”; and so forth. These arebut a few of many gestures that can be sensed as well as be mapped toparticular devices or applications, such as the advance gesture to skipto another song on a web-based radio application, a next song on acompact disk playing on a stereo, or a next page or image in a file oralbum on a computer display or digital picture frame.

Returning to FIG. 5, at 504, the method 500 transforms each of thedifferent types of type-specific raw data into a type-agnostic signalrepresentation. As noted above, these transformations can be throughdetermining range-Doppler-time profiles 506, determining range-timeprofiles 508, determining micro-Doppler profiles 510, and determiningfast-time spectrograms 512. These are described in greater detail aspart of

FIG. 2's description.

At 514, the method 500 determines, for each of the two or moretype-agnostic signal representations created at operation 504, a gestureor action of the object within the respective two or more differentradar fields.

Note that the object making the movement in each of the two or moredifferent radar fields can be a same object making a same action. Insuch a case, two different types of radar fields are used to improvegesture recognition, robustness, resolution, and so forth. Therefore,determining the gesture or action performed by the object's movement isbased, in this case, on both of the two or more type-agnostic signalrepresentations.

At 516, the method 500 passes each of the determined gestures or actionsto an application or device effective to control or alter a display,function, or capability associated with the application.

Example Computing System

FIG. 7 illustrates various components of an example computing system 700that can be implemented as any type of client, server, and/or computingdevice as described with reference to the previous FIGS. 1-6 toimplement type-agnostic RF signal representations.

The computing system 700 includes communication devices 702 that enablewired and/or wireless communication of device data 704 (e.g., receiveddata, data that is being received, data scheduled for broadcast, datapackets of the data, etc.). Device data 704 or other device content caninclude configuration settings of the device, media content stored onthe device, and/or information associated with a user of the device(e.g., an identity of an actor performing a gesture). Media contentstored on the computing system 700 can include any type of audio, video,and/or image data. The computing system 700 includes one or more datainputs 706 via which any type of data, media content, and/or inputs canbe received, such as human utterances, interactions with a radar field,user-selectable inputs (explicit or implicit), messages, music,television media content, recorded video content, and any other type ofaudio, video, and/or image data received from any content and/or datasource.

The computing system 700 also includes communication interfaces 708,which can be implemented as any one or more of a serial and/or parallelinterface, a wireless interface, any type of network interface, a modem,and as any other type of communication interface. Communicationinterfaces 708 provide a connection and/or communication links betweenthe computing system 700 and a communication network by which otherelectronic, computing, and communication devices communicate data withthe computing system 700.

The computing system 700 includes one or more processors 710 (e.g., anyof microprocessors, controllers, and the like), which process variouscomputer-executable instructions to control the operation of thecomputing system 700 and to enable techniques for, or in which can beembodied, type-agnostic RF signal representations. Alternatively or inaddition, the computing system 700 can be implemented with any one orcombination of hardware, firmware, or fixed logic circuitry that isimplemented in connection with processing and control circuits which aregenerally identified at 712. Although not shown, the computing system700 can include a system bus or data transfer system that couples thevarious components within the device. A system bus can include any oneor combination of different bus structures, such as a memory bus ormemory controller, a peripheral bus, a universal serial bus, and/or aprocessor or local bus that utilizes any of a variety of busarchitectures.

The computing system 700 also includes computer-readable media 714, suchas one or more memory devices that enable persistent and/ornon-transitory data storage (i.e., in contrast to mere signaltransmission), examples of which include random access memory (RAM),non-volatile memory (e.g., any one or more of a read-only memory (ROM),flash memory, EPROM, EEPROM, etc.), and a disk storage device. A diskstorage device may be implemented as any type of magnetic or opticalstorage device, such as a hard disk drive, a recordable and/orrewriteable compact disc (CD), any type of a digital versatile disc(DVD), and the like. The computing system 700 can also include a massstorage media device (storage media) 716.

The computer-readable media 714 provides data storage mechanisms tostore the device data 704, as well as various device applications 718and any other types of information and/or data related to operationalaspects of the computing system 700. For example, an operating system720 can be maintained as a computer application with thecomputer-readable media 714 and executed on the processors 710. Thedevice applications 718 may include a device manager, such as any formof a control application, software application, signal-processing andcontrol module, code that is native to a particular device, anabstraction module or gesture module and so on. The device applications718 also include system components, engines, or managers to implementtype-agnostic RF signal representations, such as the abstraction module122 and the recognition module 126.

The computing system 700 may also include, or have access to, one ormore of the type-specific radar systems 102, including theradar-emitting element 104 and the antenna element 106. While not shown,one or more elements of the abstraction module 122 or the recognitionmodule 126 may be operated, in whole or in part, through hardware, suchas being integrated, in whole or in part, with the type-specific radarsystems 102.

Conclusion

Although techniques using, and apparatuses including, type-agnostic RFsignal representations have been described in language specific tofeatures and/or methods, it is to be understood that the subject of theappended claims is not necessarily limited to the specific features ormethods described. Rather, the specific features and methods aredisclosed as example implementations of ways in which to determinetype-agnostic RF signal representations.

What is claimed is:
 1. At least one non-transitory computer-readablestorage medium having instructions stored thereon that, responsive toexecution by at least one computer processor, cause the computerprocessor to: receive type-specific raw data representing a reflectionsignal caused by movement of an object within a type-specific radarfield, the reflection signal comprising a superposition of reflectionsof a plurality of points of the object; transform the type-specific rawdata into a type-agnostic signal representation that is independent ofparameters of the type-specific radar field, the transformationaccording to a model of the object as a set of scattering centers, eachof the scattering centers corresponding to one of the points of theobject; and determine, based on the type-agnostic signal representation,a gesture or action performed by the object.
 2. The computer-readablestorage media of claim 1, wherein: the instructions further cause thecomputer processor to receive a complex signal based on thetype-specific raw data, the complex signal having amplitude and phaseinformation from which a phase of the type-specific raw data can beextracted and unwrapped; and the type-agnostic signal representation isbased on the phase of the type-specific raw data.
 3. Thecomputer-readable storage media of claim 1, wherein the type-agnosticsignal representation comprises a range-Doppler-time profile, range-timeprofile, micro-Doppler profile, or fast-time spectrogram for thetype-specific raw data.
 4. The computer-readable storage media of claim1, wherein: the instructions further cause the computer processor toextract a type-agnostic feature from the type-agnostic signalrepresentation, the type-agnostic feature comprising a signaltransformation, engineered feature, computer-vision feature,machine-learned feature, or inferred target feature; and thedetermination of the gesture or action performed by the object is basedon the type-agnostic feature.
 5. The computer-readable storage media ofclaim 1, wherein: the instructions further cause the processor todetermine a gesture classification, motion parameter tracking,regression estimate, or gesture probability; and the determination ofthe gesture or action performed by the object is based on the gestureclassification, motion parameter tracking, regression estimate, orgesture probability.
 6. The computer-readable storage media of claim 1,wherein: the instructions further cause the processor to: receive othertype-specific raw data representing another reflection signal caused bymovement of the object within another type-specific radar field; andtransform the other type-specific raw data into another type-agnosticsignal representation; and the determination of the gesture or actionperformed by the object is further based on the other type-agnosticsignal representation.
 7. The computer-readable storage media of claim1, wherein the parameters of the type-specific radar field comprisemodulation, frequency, amplitude, or phase parameters.
 8. Thecomputer-readable storage media of claim 1, wherein the parameterscomprise the type-specific radar field being a single tone, steppedfrequency modulated, linear frequency modulated, impulse, or chirped. 9.A computer-implemented method comprising: receiving type-specific rawdata representing a reflection signal caused by movement of an objectwithin a type-specific radar field, the reflection signal comprising asuperposition of reflections of a plurality of points of the object;transforming the type-specific raw data into a type-agnostic signalrepresentation that is independent of parameters of the type-specificradar field, the transformation according to a model of the object as aset of scattering centers, each of the scattering centers correspondingto one of the points of the object; determining, based on thetype-agnostic signal representation, a gesture or action performed bythe object; and passing the determined gesture or action to anapplication effective to control or alter a display, function, orcapability associated with the application.
 10. The method of claim 9,wherein the type-agnostic signal representation is independent ofmodulation, frequency, amplitude, or phase of the type-specific radarfield.
 11. The method of claim 9, wherein the type-agnostic signalrepresentation is independent of the type-specific radar field beingsingle tone, stepped frequency modulated, linear frequency modulated,impulse, or chirped.
 12. The method of claim 9, wherein thetype-agnostic signal representation comprises a range-Doppler profile, arange profile, a micro-Doppler profile, or a fast-time spectrogram. 13.The method of claim 9, further comprising receiving a complex signalbased on the type-specific raw data, the complex signal having amplitudeand phase information from which a phase of the type-specific raw datacan be extracted and unwrapped; and wherein the type-agnostic signalrepresentation is based on the phase of the type-specific raw data. 14.The method of claim 9, further comprising determining a gestureclassification, motion parameter tracking, regression estimate, orgesture probability; and wherein the determination of the gesture oraction performed by the object is based on the gesture classification,motion parameter tracking, regression estimate, or gesture probability.15. An apparatus comprising: at least one computer processor; atype-specific radar system configured to provide a type-specific radarfield, the type-specific radar field provided though a modulation schemeor a type of hardware radar-emitting element, the type-specific radarsystem comprising: at least one radar-emitting element configured toprovide the type-specific radar field; and at least one antenna elementconfigured to receive a reflection signal caused by an object moving inthe type-specific radar field; and at least one computer-readablestorage medium having instructions stored thereon that, responsive toexecution by the computer processor, cause the computer processor to:receive type-specific raw data representing the reflection signal causedby movement of the object within the type-specific radar field, thereflection signal comprising a superposition of reflections of aplurality of points of the object; transform the type-specific raw datainto a type-agnostic signal representation that is independent ofparameters of the type-specific radar field, the transformationaccording to a model of the object as a set of scattering centers, eachof the scattering centers corresponding to one of the points of theobject; determine, based on the type-agnostic signal representation, agesture or action performed by the object; and pass the determinedgesture or action to an application effective to control or alter adisplay, function, or capability associated with the application. 16.The apparatus of claim 15, wherein the type-agnostic signalrepresentation comprises a range-Doppler profile, a range profile, amicro-Doppler profile, or a fast-time spectrogram.
 17. The apparatus ofclaim 15, wherein: the instructions further cause the processor toreceive a complex signal based on the type-specific raw data, thecomplex signal having amplitude and phase information from which a phaseof the type-specific raw data can be extracted and unwrapped; and thetype-agnostic signal representation is based on the phase of thetype-specific raw data.
 18. The apparatus of claim 15, wherein: theinstructions further cause the processor to determine a gestureclassification, motion parameter tracking, regression estimate, orgesture probability; and the determination of the gesture or actionperformed by the object is based on the gesture classification, motionparameter tracking, regression estimate, or gesture probability.
 19. Theapparatus of claim 15, wherein the apparatus is a mobile computingdevice having the display.
 20. The apparatus of claim 19, wherein thedetermined gesture or action is a gesture controlling a user interfaceassociated with the application and presented on the display.